Overview

Dataset statistics

Number of variables21
Number of observations115520
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.4 MiB
Average record size in memory176.0 B

Variable types

Text8
Categorical4
DateTime1
Numeric8

Alerts

customer_state is highly overall correlated with customer_zip_code_prefixHigh correlation
customer_zip_code_prefix is highly overall correlated with customer_stateHigh correlation
payment_value is highly overall correlated with priceHigh correlation
price is highly overall correlated with payment_valueHigh correlation
seller_state is highly overall correlated with seller_zip_code_prefixHigh correlation
seller_zip_code_prefix is highly overall correlated with seller_stateHigh correlation
order_status is highly imbalanced (92.8%)Imbalance
seller_state is highly imbalanced (62.7%)Imbalance

Reproduction

Analysis started2024-08-09 18:56:35.439458
Analysis finished2024-08-09 18:56:53.890773
Duration18.45 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Distinct96967
Distinct (%)83.9%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2024-08-09T18:56:54.121009image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3696640
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique84736 ?
Unique (%)73.4%

Sample

1st rowe481f51cbdc54678b7cc49136f2d6af7
2nd rowe481f51cbdc54678b7cc49136f2d6af7
3rd rowe481f51cbdc54678b7cc49136f2d6af7
4th row128e10d95713541c87cd1a2e48201934
5th row0e7e841ddf8f8f2de2bad69267ecfbcf
ValueCountFrequency (%)
895ab968e7bb0d5659d16cd74cd1650c 63
 
0.1%
fedcd9f7ccdc8cba3a18defedd1a5547 38
 
< 0.1%
fa65dad1b0e818e3ccc5cb0e39231352 29
 
< 0.1%
ccf804e764ed5650cd8759557269dc13 26
 
< 0.1%
465c2e1bee4561cb39e0db8c5993aafc 24
 
< 0.1%
c6492b842ac190db807c15aff21a7dd6 24
 
< 0.1%
a3725dfe487d359b5be08cac48b64ec5 24
 
< 0.1%
68986e4324f6a21481df4e6e89abcf01 24
 
< 0.1%
285c2e15bebd4ac83635ccc563dc71f4 22
 
< 0.1%
1c11d0f4353b31ac3417fbfa5f0f2a8a 21
 
< 0.1%
Other values (96957) 115225
99.7%
2024-08-09T18:56:54.485948image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 232173
 
6.3%
b 232045
 
6.3%
6 231977
 
6.3%
e 231746
 
6.3%
3 231264
 
6.3%
c 231256
 
6.3%
7 231168
 
6.3%
8 231132
 
6.3%
1 231097
 
6.3%
a 230987
 
6.2%
Other values (6) 1381795
37.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2309815
62.5%
Lowercase Letter 1386825
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 232173
10.1%
6 231977
10.0%
3 231264
10.0%
7 231168
10.0%
8 231132
10.0%
1 231097
10.0%
2 230705
10.0%
9 230509
10.0%
0 229943
10.0%
5 229847
10.0%
Lowercase Letter
ValueCountFrequency (%)
b 232045
16.7%
e 231746
16.7%
c 231256
16.7%
a 230987
16.7%
f 230671
16.6%
d 230120
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common 2309815
62.5%
Latin 1386825
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
4 232173
10.1%
6 231977
10.0%
3 231264
10.0%
7 231168
10.0%
8 231132
10.0%
1 231097
10.0%
2 230705
10.0%
9 230509
10.0%
0 229943
10.0%
5 229847
10.0%
Latin
ValueCountFrequency (%)
b 232045
16.7%
e 231746
16.7%
c 231256
16.7%
a 230987
16.7%
f 230671
16.6%
d 230120
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3696640
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 232173
 
6.3%
b 232045
 
6.3%
6 231977
 
6.3%
e 231746
 
6.3%
3 231264
 
6.3%
c 231256
 
6.3%
7 231168
 
6.3%
8 231132
 
6.3%
1 231097
 
6.3%
a 230987
 
6.2%
Other values (6) 1381795
37.4%
Distinct96967
Distinct (%)83.9%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2024-08-09T18:56:54.791538image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3696640
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique84736 ?
Unique (%)73.4%

Sample

1st row9ef432eb6251297304e76186b10a928d
2nd row9ef432eb6251297304e76186b10a928d
3rd row9ef432eb6251297304e76186b10a928d
4th rowa20e8105f23924cd00833fd87daa0831
5th row26c7ac168e1433912a51b924fbd34d34
ValueCountFrequency (%)
270c23a11d024a44c896d1894b261a83 63
 
0.1%
13aa59158da63ba0e93ec6ac2c07aacb 38
 
< 0.1%
9af2372a1e49340278e7c1ef8d749f34 29
 
< 0.1%
92cd3ec6e2d643d4ebd0e3d6238f69e2 26
 
< 0.1%
63b964e79dee32a3587651701a2b8dbf 24
 
< 0.1%
6ee2f17e3b6c33d6a9557f280edd2925 24
 
< 0.1%
d22f25a9fadfb1abbc2e29395b1239f4 24
 
< 0.1%
86cc80fef09f7f39df4b0dbce48e81cb 24
 
< 0.1%
b246eeed30b362c09d867b9e598bee51 22
 
< 0.1%
50920f8cd0681fd86ebe93670c8fe52e 21
 
< 0.1%
Other values (96957) 115225
99.7%
2024-08-09T18:56:55.165263image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 231689
 
6.3%
f 231598
 
6.3%
5 231557
 
6.3%
c 231500
 
6.3%
6 231348
 
6.3%
1 231308
 
6.3%
8 231128
 
6.3%
a 231051
 
6.3%
3 230987
 
6.2%
7 230945
 
6.2%
Other values (6) 1383529
37.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2309791
62.5%
Lowercase Letter 1386849
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 231689
10.0%
5 231557
10.0%
6 231348
10.0%
1 231308
10.0%
8 231128
10.0%
3 230987
10.0%
7 230945
10.0%
9 230834
10.0%
4 230085
10.0%
0 229910
10.0%
Lowercase Letter
ValueCountFrequency (%)
f 231598
16.7%
c 231500
16.7%
a 231051
16.7%
d 230922
16.7%
e 230918
16.7%
b 230860
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common 2309791
62.5%
Latin 1386849
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
2 231689
10.0%
5 231557
10.0%
6 231348
10.0%
1 231308
10.0%
8 231128
10.0%
3 230987
10.0%
7 230945
10.0%
9 230834
10.0%
4 230085
10.0%
0 229910
10.0%
Latin
ValueCountFrequency (%)
f 231598
16.7%
c 231500
16.7%
a 231051
16.7%
d 230922
16.7%
e 230918
16.7%
b 230860
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3696640
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 231689
 
6.3%
f 231598
 
6.3%
5 231557
 
6.3%
c 231500
 
6.3%
6 231348
 
6.3%
1 231308
 
6.3%
8 231128
 
6.3%
a 231051
 
6.3%
3 230987
 
6.2%
7 230945
 
6.2%
Other values (6) 1383529
37.4%

order_status
Categorical

IMBALANCE 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
delivered
113078 
shipped
 
1203
canceled
 
536
processing
 
360
invoiced
 
340

Length

Max length10
Median length9
Mean length8.9746797
Min length7

Characters and Unicode

Total characters1036755
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdelivered
2nd rowdelivered
3rd rowdelivered
4th rowdelivered
5th rowdelivered

Common Values

ValueCountFrequency (%)
delivered 113078
97.9%
shipped 1203
 
1.0%
canceled 536
 
0.5%
processing 360
 
0.3%
invoiced 340
 
0.3%
approved 3
 
< 0.1%

Length

2024-08-09T18:56:55.288361image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-09T18:56:55.360074image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
delivered 113078
97.9%
shipped 1203
 
1.0%
canceled 536
 
0.5%
processing 360
 
0.3%
invoiced 340
 
0.3%
approved 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 342212
33.0%
d 228238
22.0%
i 115321
 
11.1%
l 113614
 
11.0%
r 113441
 
10.9%
v 113421
 
10.9%
p 2772
 
0.3%
s 1923
 
0.2%
c 1772
 
0.2%
n 1236
 
0.1%
Other values (4) 2805
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1036755
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 342212
33.0%
d 228238
22.0%
i 115321
 
11.1%
l 113614
 
11.0%
r 113441
 
10.9%
v 113421
 
10.9%
p 2772
 
0.3%
s 1923
 
0.2%
c 1772
 
0.2%
n 1236
 
0.1%
Other values (4) 2805
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 1036755
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 342212
33.0%
d 228238
22.0%
i 115321
 
11.1%
l 113614
 
11.0%
r 113441
 
10.9%
v 113421
 
10.9%
p 2772
 
0.3%
s 1923
 
0.2%
c 1772
 
0.2%
n 1236
 
0.1%
Other values (4) 2805
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1036755
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 342212
33.0%
d 228238
22.0%
i 115321
 
11.1%
l 113614
 
11.0%
r 113441
 
10.9%
v 113421
 
10.9%
p 2772
 
0.3%
s 1923
 
0.2%
c 1772
 
0.2%
n 1236
 
0.1%
Other values (4) 2805
 
0.3%
Distinct96432
Distinct (%)83.5%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Minimum2017-01-05 11:56:06
Maximum2018-09-03 09:06:57
2024-08-09T18:56:55.434760image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:55.519318image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

order_item_id
Real number (ℝ)

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1961305
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-08-09T18:56:55.589732image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum21
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.69939972
Coefficient of variation (CV)0.58471855
Kurtosis104.79589
Mean1.1961305
Median Absolute Deviation (MAD)0
Skewness7.600338
Sum138177
Variance0.48915997
MonotonicityNot monotonic
2024-08-09T18:56:55.665245image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 101228
87.6%
2 10059
 
8.7%
3 2333
 
2.0%
4 969
 
0.8%
5 459
 
0.4%
6 258
 
0.2%
7 60
 
0.1%
8 35
 
< 0.1%
9 28
 
< 0.1%
10 25
 
< 0.1%
Other values (11) 66
 
0.1%
ValueCountFrequency (%)
1 101228
87.6%
2 10059
 
8.7%
3 2333
 
2.0%
4 969
 
0.8%
5 459
 
0.4%
6 258
 
0.2%
7 60
 
0.1%
8 35
 
< 0.1%
9 28
 
< 0.1%
10 25
 
< 0.1%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 3
 
< 0.1%
19 3
 
< 0.1%
18 3
 
< 0.1%
17 3
 
< 0.1%
16 3
 
< 0.1%
15 5
 
< 0.1%
14 7
< 0.1%
13 8
< 0.1%
12 13
< 0.1%
Distinct32177
Distinct (%)27.9%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2024-08-09T18:56:55.867200image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3696640
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16995 ?
Unique (%)14.7%

Sample

1st row87285b34884572647811a353c7ac498a
2nd row87285b34884572647811a353c7ac498a
3rd row87285b34884572647811a353c7ac498a
4th row87285b34884572647811a353c7ac498a
5th row87285b34884572647811a353c7ac498a
ValueCountFrequency (%)
aca2eb7d00ea1a7b8ebd4e68314663af 536
 
0.5%
99a4788cb24856965c36a24e339b6058 525
 
0.5%
422879e10f46682990de24d770e7f83d 505
 
0.4%
389d119b48cf3043d311335e499d9c6b 406
 
0.4%
368c6c730842d78016ad823897a372db 395
 
0.3%
53759a2ecddad2bb87a079a1f1519f73 389
 
0.3%
d1c427060a0f73f6b889a5c7c61f2ac4 357
 
0.3%
53b36df67ebb7c41585e8d54d6772e08 327
 
0.3%
154e7e31ebfa092203795c972e5804a6 283
 
0.2%
3dd2a17168ec895c781a9191c1e95ad7 278
 
0.2%
Other values (32167) 111519
96.5%
2024-08-09T18:56:56.172561image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 237738
 
6.4%
9 235906
 
6.4%
e 233463
 
6.3%
7 232907
 
6.3%
8 232402
 
6.3%
4 231211
 
6.3%
a 231088
 
6.3%
2 230852
 
6.2%
c 230814
 
6.2%
0 230643
 
6.2%
Other values (6) 1369616
37.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2320423
62.8%
Lowercase Letter 1376217
37.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 237738
10.2%
9 235906
10.2%
7 232907
10.0%
8 232402
10.0%
4 231211
10.0%
2 230852
9.9%
0 230643
9.9%
6 230580
9.9%
5 229702
9.9%
1 228482
9.8%
Lowercase Letter
ValueCountFrequency (%)
e 233463
17.0%
a 231088
16.8%
c 230814
16.8%
b 229304
16.7%
d 227109
16.5%
f 224439
16.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2320423
62.8%
Latin 1376217
37.2%

Most frequent character per script

Common
ValueCountFrequency (%)
3 237738
10.2%
9 235906
10.2%
7 232907
10.0%
8 232402
10.0%
4 231211
10.0%
2 230852
9.9%
0 230643
9.9%
6 230580
9.9%
5 229702
9.9%
1 228482
9.8%
Latin
ValueCountFrequency (%)
e 233463
17.0%
a 231088
16.8%
c 230814
16.8%
b 229304
16.7%
d 227109
16.5%
f 224439
16.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3696640
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 237738
 
6.4%
9 235906
 
6.4%
e 233463
 
6.3%
7 232907
 
6.3%
8 232402
 
6.3%
4 231211
 
6.3%
a 231088
 
6.3%
2 230852
 
6.2%
c 230814
 
6.2%
0 230643
 
6.2%
Other values (6) 1369616
37.1%
Distinct3007
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2024-08-09T18:56:56.401110image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3696640
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique473 ?
Unique (%)0.4%

Sample

1st row3504c0cb71d7fa48d967e0e4c94d59d9
2nd row3504c0cb71d7fa48d967e0e4c94d59d9
3rd row3504c0cb71d7fa48d967e0e4c94d59d9
4th row3504c0cb71d7fa48d967e0e4c94d59d9
5th row3504c0cb71d7fa48d967e0e4c94d59d9
ValueCountFrequency (%)
4a3ca9315b744ce9f8e9374361493884 2133
 
1.8%
6560211a19b47992c3666cc44a7e94c0 2122
 
1.8%
1f50f920176fa81dab994f9023523100 2008
 
1.7%
cc419e0650a3c5ba77189a1882b7556a 1847
 
1.6%
da8622b14eb17ae2831f4ac5b9dab84a 1639
 
1.4%
955fee9216a65b617aa5c0531780ce60 1528
 
1.3%
1025f0e2d44d7041d6cf58b6550e0bfa 1462
 
1.3%
7c67e1448b00f6e969d365cea6b010ab 1452
 
1.3%
7a67c85e85bb2ce8582c35f2203ad736 1240
 
1.1%
ea8482cd71df3c1969d7b9473ff13abc 1239
 
1.1%
Other values (2997) 98850
85.6%
2024-08-09T18:56:56.698802image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 251809
 
6.8%
c 243363
 
6.6%
4 243019
 
6.6%
6 238051
 
6.4%
0 237041
 
6.4%
a 236536
 
6.4%
b 235438
 
6.4%
3 235225
 
6.4%
9 228750
 
6.2%
2 227554
 
6.2%
Other values (6) 1319854
35.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2338305
63.3%
Lowercase Letter 1358335
36.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 251809
10.8%
4 243019
10.4%
6 238051
10.2%
0 237041
10.1%
3 235225
10.1%
9 228750
9.8%
2 227554
9.7%
8 226090
9.7%
5 225810
9.7%
7 224956
9.6%
Lowercase Letter
ValueCountFrequency (%)
c 243363
17.9%
a 236536
17.4%
b 235438
17.3%
e 216418
15.9%
f 214487
15.8%
d 212093
15.6%

Most occurring scripts

ValueCountFrequency (%)
Common 2338305
63.3%
Latin 1358335
36.7%

Most frequent character per script

Common
ValueCountFrequency (%)
1 251809
10.8%
4 243019
10.4%
6 238051
10.2%
0 237041
10.1%
3 235225
10.1%
9 228750
9.8%
2 227554
9.7%
8 226090
9.7%
5 225810
9.7%
7 224956
9.6%
Latin
ValueCountFrequency (%)
c 243363
17.9%
a 236536
17.4%
b 235438
17.3%
e 216418
15.9%
f 214487
15.8%
d 212093
15.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3696640
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 251809
 
6.8%
c 243363
 
6.6%
4 243019
 
6.6%
6 238051
 
6.4%
0 237041
 
6.4%
a 236536
 
6.4%
b 235438
 
6.4%
3 235225
 
6.4%
9 228750
 
6.2%
2 227554
 
6.2%
Other values (6) 1319854
35.7%

price
Real number (ℝ)

HIGH CORRELATION 

Distinct5897
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.90668
Minimum0.85
Maximum6735
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-08-09T18:56:56.816042image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.85
5-th percentile17
Q139.9
median74.9
Q3134.9
95-th percentile349.9
Maximum6735
Range6734.15
Interquartile range (IQR)95

Descriptive statistics

Standard deviation184.28063
Coefficient of variation (CV)1.524156
Kurtosis118.82035
Mean120.90668
Median Absolute Deviation (MAD)42
Skewness7.8622591
Sum13967139
Variance33959.351
MonotonicityNot monotonic
2024-08-09T18:56:56.901231image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.9 2558
 
2.2%
69.9 2079
 
1.8%
49.9 2009
 
1.7%
89.9 1599
 
1.4%
99.9 1490
 
1.3%
29.9 1356
 
1.2%
39.9 1327
 
1.1%
19.9 1268
 
1.1%
79.9 1261
 
1.1%
29.99 1209
 
1.0%
Other values (5887) 99364
86.0%
ValueCountFrequency (%)
0.85 3
 
< 0.1%
1.2 20
< 0.1%
2.2 2
 
< 0.1%
2.29 1
 
< 0.1%
2.9 1
 
< 0.1%
2.99 1
 
< 0.1%
3.06 3
 
< 0.1%
3.49 3
 
< 0.1%
3.5 7
 
< 0.1%
3.54 1
 
< 0.1%
ValueCountFrequency (%)
6735 1
< 0.1%
6729 1
< 0.1%
6499 1
< 0.1%
4799 1
< 0.1%
4690 1
< 0.1%
4590 1
< 0.1%
4399.87 1
< 0.1%
4099.99 1
< 0.1%
4059 1
< 0.1%
3999.9 1
< 0.1%

freight_value
Real number (ℝ)

Distinct6966
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.075696
Minimum0
Maximum409.68
Zeros388
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-08-09T18:56:56.986524image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.78
Q113.08
median16.32
Q321.22
95-th percentile45.4
Maximum409.68
Range409.68
Interquartile range (IQR)8.14

Descriptive statistics

Standard deviation15.87439
Coefficient of variation (CV)0.79072673
Kurtosis57.929779
Mean20.075696
Median Absolute Deviation (MAD)3.63
Skewness5.5501683
Sum2319144.4
Variance251.99625
MonotonicityNot monotonic
2024-08-09T18:56:57.066987image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.1 3758
 
3.3%
7.78 2289
 
2.0%
14.1 1933
 
1.7%
11.85 1933
 
1.7%
18.23 1602
 
1.4%
7.39 1554
 
1.3%
16.11 1178
 
1.0%
15.23 1047
 
0.9%
8.72 943
 
0.8%
16.79 906
 
0.8%
Other values (6956) 98377
85.2%
ValueCountFrequency (%)
0 388
0.3%
0.01 4
 
< 0.1%
0.02 3
 
< 0.1%
0.03 14
 
< 0.1%
0.04 4
 
< 0.1%
0.05 3
 
< 0.1%
0.06 13
 
< 0.1%
0.07 1
 
< 0.1%
0.08 12
 
< 0.1%
0.09 6
 
< 0.1%
ValueCountFrequency (%)
409.68 1
< 0.1%
375.28 2
< 0.1%
339.59 1
< 0.1%
338.3 1
< 0.1%
322.1 1
< 0.1%
321.88 1
< 0.1%
321.46 1
< 0.1%
317.47 1
< 0.1%
314.4 1
< 0.1%
314.02 1
< 0.1%

payment_sequential
Real number (ℝ)

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0928238
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-08-09T18:56:57.139574image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum29
Range28
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.72581133
Coefficient of variation (CV)0.66416138
Kurtosis357.32827
Mean1.0928238
Median Absolute Deviation (MAD)0
Skewness16.16416
Sum126243
Variance0.52680209
MonotonicityNot monotonic
2024-08-09T18:56:57.211375image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1 110593
95.7%
2 3302
 
2.9%
3 631
 
0.5%
4 303
 
0.3%
5 180
 
0.2%
6 124
 
0.1%
7 85
 
0.1%
8 56
 
< 0.1%
9 46
 
< 0.1%
10 39
 
< 0.1%
Other values (19) 161
 
0.1%
ValueCountFrequency (%)
1 110593
95.7%
2 3302
 
2.9%
3 631
 
0.5%
4 303
 
0.3%
5 180
 
0.2%
6 124
 
0.1%
7 85
 
0.1%
8 56
 
< 0.1%
9 46
 
< 0.1%
10 39
 
< 0.1%
ValueCountFrequency (%)
29 1
 
< 0.1%
28 1
 
< 0.1%
27 1
 
< 0.1%
26 2
 
< 0.1%
25 2
 
< 0.1%
24 2
 
< 0.1%
23 2
 
< 0.1%
22 3
< 0.1%
21 6
< 0.1%
20 6
< 0.1%

payment_type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
credit_card
85246 
boleto
22479 
voucher
 
6134
debit_card
 
1661

Length

Max length11
Median length11
Mean length9.800277
Min length6

Characters and Unicode

Total characters1132128
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcredit_card
2nd rowvoucher
3rd rowvoucher
4th rowcredit_card
5th rowcredit_card

Common Values

ValueCountFrequency (%)
credit_card 85246
73.8%
boleto 22479
 
19.5%
voucher 6134
 
5.3%
debit_card 1661
 
1.4%

Length

2024-08-09T18:56:57.285572image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-09T18:56:57.344611image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
credit_card 85246
73.8%
boleto 22479
 
19.5%
voucher 6134
 
5.3%
debit_card 1661
 
1.4%

Most occurring characters

ValueCountFrequency (%)
c 178287
15.7%
r 178287
15.7%
d 173814
15.4%
e 115520
10.2%
t 109386
9.7%
i 86907
7.7%
_ 86907
7.7%
a 86907
7.7%
o 51092
 
4.5%
b 24140
 
2.1%
Other values (4) 40881
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1045221
92.3%
Connector Punctuation 86907
 
7.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 178287
17.1%
r 178287
17.1%
d 173814
16.6%
e 115520
11.1%
t 109386
10.5%
i 86907
8.3%
a 86907
8.3%
o 51092
 
4.9%
b 24140
 
2.3%
l 22479
 
2.2%
Other values (3) 18402
 
1.8%
Connector Punctuation
ValueCountFrequency (%)
_ 86907
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1045221
92.3%
Common 86907
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 178287
17.1%
r 178287
17.1%
d 173814
16.6%
e 115520
11.1%
t 109386
10.5%
i 86907
8.3%
a 86907
8.3%
o 51092
 
4.9%
b 24140
 
2.3%
l 22479
 
2.2%
Other values (3) 18402
 
1.8%
Common
ValueCountFrequency (%)
_ 86907
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1132128
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 178287
15.7%
r 178287
15.7%
d 173814
15.4%
e 115520
10.2%
t 109386
9.7%
i 86907
7.7%
_ 86907
7.7%
a 86907
7.7%
o 51092
 
4.5%
b 24140
 
2.1%
Other values (4) 40881
 
3.6%

payment_installments
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9427285
Minimum0
Maximum24
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-08-09T18:56:57.409483image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q34
95-th percentile10
Maximum24
Range24
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.7780283
Coefficient of variation (CV)0.94403145
Kurtosis2.5320115
Mean2.9427285
Median Absolute Deviation (MAD)1
Skewness1.6216668
Sum339944
Variance7.7174412
MonotonicityNot monotonic
2024-08-09T18:56:57.480914image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 57573
49.8%
2 13437
 
11.6%
3 11535
 
10.0%
4 7854
 
6.8%
10 6735
 
5.8%
5 5918
 
5.1%
8 5007
 
4.3%
6 4534
 
3.9%
7 1779
 
1.5%
9 717
 
0.6%
Other values (14) 431
 
0.4%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 57573
49.8%
2 13437
 
11.6%
3 11535
 
10.0%
4 7854
 
6.8%
5 5918
 
5.1%
6 4534
 
3.9%
7 1779
 
1.5%
8 5007
 
4.3%
9 717
 
0.6%
ValueCountFrequency (%)
24 34
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
21 5
 
< 0.1%
20 21
 
< 0.1%
18 38
< 0.1%
17 7
 
< 0.1%
16 7
 
< 0.1%
15 92
0.1%
14 16
 
< 0.1%

payment_value
Real number (ℝ)

HIGH CORRELATION 

Distinct28685
Distinct (%)24.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean172.96699
Minimum0
Maximum13664.08
Zeros6
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-08-09T18:56:57.556381image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27.29
Q161.01
median108.2
Q3189.5925
95-th percentile516.2515
Maximum13664.08
Range13664.08
Interquartile range (IQR)128.5825

Descriptive statistics

Standard deviation268.30463
Coefficient of variation (CV)1.5511897
Kurtosis510.5349
Mean172.96699
Median Absolute Deviation (MAD)56.7
Skewness14.15703
Sum19981147
Variance71987.372
MonotonicityNot monotonic
2024-08-09T18:56:57.640009image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 339
 
0.3%
100 296
 
0.3%
20 281
 
0.2%
77.57 249
 
0.2%
35 163
 
0.1%
73.34 158
 
0.1%
30 134
 
0.1%
116.94 130
 
0.1%
56.78 122
 
0.1%
25 117
 
0.1%
Other values (28675) 113531
98.3%
ValueCountFrequency (%)
0 6
< 0.1%
0.01 6
< 0.1%
0.03 2
 
< 0.1%
0.05 2
 
< 0.1%
0.08 2
 
< 0.1%
0.09 1
 
< 0.1%
0.1 3
< 0.1%
0.11 2
 
< 0.1%
0.13 1
 
< 0.1%
0.14 5
< 0.1%
ValueCountFrequency (%)
13664.08 8
< 0.1%
7274.88 4
< 0.1%
6929.31 1
 
< 0.1%
6922.21 1
 
< 0.1%
6726.66 1
 
< 0.1%
6081.54 6
< 0.1%
4950.34 1
 
< 0.1%
4809.44 2
 
< 0.1%
4764.34 1
 
< 0.1%
4681.78 1
 
< 0.1%
Distinct73
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2024-08-09T18:56:57.793728image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length45
Median length33
Mean length12.98608
Min length3

Characters and Unicode

Total characters1500152
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhousewares
2nd rowhousewares
3rd rowhousewares
4th rowhousewares
5th rowhousewares
ValueCountFrequency (%)
bed_bath_table 11815
 
10.2%
health_beauty 9922
 
8.6%
sports_leisure 8926
 
7.7%
furniture_decor 8667
 
7.5%
computers_accessories 8058
 
7.0%
housewares 7343
 
6.4%
watches_gifts 6196
 
5.4%
telephony 4710
 
4.1%
garden_tools 4569
 
4.0%
auto 4367
 
3.8%
Other values (63) 40947
35.4%
2024-08-09T18:56:58.047591image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 183479
12.2%
s 140998
 
9.4%
t 132194
 
8.8%
o 111040
 
7.4%
r 104702
 
7.0%
a 101494
 
6.8%
_ 101213
 
6.7%
u 77390
 
5.2%
c 71817
 
4.8%
i 62761
 
4.2%
Other values (16) 413064
27.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1398640
93.2%
Connector Punctuation 101213
 
6.7%
Decimal Number 299
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 183479
13.1%
s 140998
 
10.1%
t 132194
 
9.5%
o 111040
 
7.9%
r 104702
 
7.5%
a 101494
 
7.3%
u 77390
 
5.5%
c 71817
 
5.1%
i 62761
 
4.5%
h 59108
 
4.2%
Other values (14) 353657
25.3%
Connector Punctuation
ValueCountFrequency (%)
_ 101213
100.0%
Decimal Number
ValueCountFrequency (%)
2 299
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1398640
93.2%
Common 101512
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 183479
13.1%
s 140998
 
10.1%
t 132194
 
9.5%
o 111040
 
7.9%
r 104702
 
7.5%
a 101494
 
7.3%
u 77390
 
5.5%
c 71817
 
5.1%
i 62761
 
4.5%
h 59108
 
4.2%
Other values (14) 353657
25.3%
Common
ValueCountFrequency (%)
_ 101213
99.7%
2 299
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1500152
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 183479
12.2%
s 140998
 
9.4%
t 132194
 
8.8%
o 111040
 
7.4%
r 104702
 
7.0%
a 101494
 
6.8%
_ 101213
 
6.7%
u 77390
 
5.2%
c 71817
 
4.8%
i 62761
 
4.2%
Other values (16) 413064
27.5%
Distinct93811
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2024-08-09T18:56:58.324389image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3696640
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique79760 ?
Unique (%)69.0%

Sample

1st row7c396fd4830fd04220f754e42b4e5bff
2nd row7c396fd4830fd04220f754e42b4e5bff
3rd row7c396fd4830fd04220f754e42b4e5bff
4th row3a51803cc0d012c3b5dc8b7528cb05f7
5th rowef0996a1a279c26e7ecbd737be23d235
ValueCountFrequency (%)
9a736b248f67d166d2fbb006bcb877c3 75
 
0.1%
6fbc7cdadbb522125f4b27ae9dee4060 38
 
< 0.1%
f9ae226291893fda10af7965268fb7f6 35
 
< 0.1%
8af7ac63b2efbcbd88e5b11505e8098a 29
 
< 0.1%
569aa12b73b5f7edeaa6f2a01603e381 26
 
< 0.1%
5419a7c9b86a43d8140e2939cd2c2f7e 24
 
< 0.1%
c8460e4251689ba205045f3ea17884a1 24
 
< 0.1%
85963fd37bfd387aa6d915d8a1065486 24
 
< 0.1%
db1af3fd6b23ac3873ef02619d548f9c 24
 
< 0.1%
2524dcec233c3766f2c2b22f69fd65f4 22
 
< 0.1%
Other values (93801) 115199
99.7%
2024-08-09T18:56:58.676342image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 232052
 
6.3%
1 231648
 
6.3%
b 231626
 
6.3%
a 231329
 
6.3%
3 231258
 
6.3%
d 231253
 
6.3%
8 231099
 
6.3%
e 230978
 
6.2%
9 230972
 
6.2%
2 230959
 
6.2%
Other values (6) 1383466
37.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2310327
62.5%
Lowercase Letter 1386313
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 232052
10.0%
1 231648
10.0%
3 231258
10.0%
8 231099
10.0%
9 230972
10.0%
2 230959
10.0%
5 230899
10.0%
0 230749
10.0%
7 230639
10.0%
4 230052
10.0%
Lowercase Letter
ValueCountFrequency (%)
b 231626
16.7%
a 231329
16.7%
d 231253
16.7%
e 230978
16.7%
f 230755
16.6%
c 230372
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common 2310327
62.5%
Latin 1386313
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
6 232052
10.0%
1 231648
10.0%
3 231258
10.0%
8 231099
10.0%
9 230972
10.0%
2 230959
10.0%
5 230899
10.0%
0 230749
10.0%
7 230639
10.0%
4 230052
10.0%
Latin
ValueCountFrequency (%)
b 231626
16.7%
a 231329
16.7%
d 231253
16.7%
e 230978
16.7%
f 230755
16.6%
c 230372
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3696640
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 232052
 
6.3%
1 231648
 
6.3%
b 231626
 
6.3%
a 231329
 
6.3%
3 231258
 
6.3%
d 231253
 
6.3%
8 231099
 
6.3%
e 230978
 
6.2%
9 230972
 
6.2%
2 230959
 
6.2%
Other values (6) 1383466
37.4%

customer_zip_code_prefix
Real number (ℝ)

HIGH CORRELATION 

Distinct14923
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35035.811
Minimum1003
Maximum99980
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-08-09T18:56:58.796180image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1003
5-th percentile3286
Q111310
median24315
Q358428
95-th percentile90560
Maximum99980
Range98977
Interquartile range (IQR)47118

Descriptive statistics

Standard deviation29808.18
Coefficient of variation (CV)0.85079177
Kurtosis-0.7798954
Mean35035.811
Median Absolute Deviation (MAD)16294
Skewness0.7855617
Sum4.0473369 × 109
Variance8.8852759 × 108
MonotonicityNot monotonic
2024-08-09T18:56:59.000865image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24220 154
 
0.1%
22790 153
 
0.1%
22793 151
 
0.1%
24230 134
 
0.1%
22775 125
 
0.1%
35162 122
 
0.1%
29101 119
 
0.1%
11740 110
 
0.1%
13087 106
 
0.1%
36570 104
 
0.1%
Other values (14913) 114242
98.9%
ValueCountFrequency (%)
1003 1
 
< 0.1%
1004 2
 
< 0.1%
1005 6
< 0.1%
1006 2
 
< 0.1%
1007 4
< 0.1%
1008 4
< 0.1%
1009 8
< 0.1%
1011 6
< 0.1%
1012 2
 
< 0.1%
1013 3
 
< 0.1%
ValueCountFrequency (%)
99980 3
 
< 0.1%
99970 1
 
< 0.1%
99965 2
 
< 0.1%
99960 1
 
< 0.1%
99955 3
 
< 0.1%
99950 8
< 0.1%
99940 2
 
< 0.1%
99930 5
< 0.1%
99925 1
 
< 0.1%
99920 1
 
< 0.1%
Distinct4092
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2024-08-09T18:56:59.201165image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length32
Median length27
Mean length10.332185
Min length3

Characters and Unicode

Total characters1193574
Distinct characters31
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1032 ?
Unique (%)0.9%

Sample

1st rowsao paulo
2nd rowsao paulo
3rd rowsao paulo
4th rowsao paulo
5th rowsao paulo
ValueCountFrequency (%)
sao 24589
 
12.1%
paulo 18318
 
9.0%
de 11259
 
5.6%
rio 9636
 
4.8%
janeiro 8020
 
4.0%
do 4964
 
2.5%
belo 3249
 
1.6%
horizonte 3205
 
1.6%
brasilia 2427
 
1.2%
porto 1933
 
1.0%
Other values (3266) 114881
56.7%
2024-08-09T18:56:59.516420image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 196998
16.5%
o 147185
12.3%
i 91289
 
7.6%
r 88470
 
7.4%
86961
 
7.3%
e 77451
 
6.5%
s 73129
 
6.1%
n 52865
 
4.4%
u 52421
 
4.4%
l 52003
 
4.4%
Other values (21) 274802
23.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1106074
92.7%
Space Separator 86961
 
7.3%
Dash Punctuation 278
 
< 0.1%
Other Punctuation 259
 
< 0.1%
Decimal Number 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 196998
17.8%
o 147185
13.3%
i 91289
 
8.3%
r 88470
 
8.0%
e 77451
 
7.0%
s 73129
 
6.6%
n 52865
 
4.8%
u 52421
 
4.7%
l 52003
 
4.7%
p 43379
 
3.9%
Other values (16) 230884
20.9%
Decimal Number
ValueCountFrequency (%)
1 1
50.0%
4 1
50.0%
Space Separator
ValueCountFrequency (%)
86961
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 278
100.0%
Other Punctuation
ValueCountFrequency (%)
' 259
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1106074
92.7%
Common 87500
 
7.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 196998
17.8%
o 147185
13.3%
i 91289
 
8.3%
r 88470
 
8.0%
e 77451
 
7.0%
s 73129
 
6.6%
n 52865
 
4.8%
u 52421
 
4.7%
l 52003
 
4.7%
p 43379
 
3.9%
Other values (16) 230884
20.9%
Common
ValueCountFrequency (%)
86961
99.4%
- 278
 
0.3%
' 259
 
0.3%
1 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1193574
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 196998
16.5%
o 147185
12.3%
i 91289
 
7.6%
r 88470
 
7.4%
86961
 
7.3%
e 77451
 
6.5%
s 73129
 
6.1%
n 52865
 
4.4%
u 52421
 
4.4%
l 52003
 
4.4%
Other values (21) 274802
23.0%

customer_state
Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
SP
48724 
RJ
15029 
MG
13406 
RS
6359 
PR
5850 
Other values (22)
26152 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters231040
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSP
2nd rowSP
3rd rowSP
4th rowSP
5th rowSP

Common Values

ValueCountFrequency (%)
SP 48724
42.2%
RJ 15029
 
13.0%
MG 13406
 
11.6%
RS 6359
 
5.5%
PR 5850
 
5.1%
SC 4223
 
3.7%
BA 3971
 
3.4%
DF 2432
 
2.1%
GO 2367
 
2.0%
ES 2315
 
2.0%
Other values (17) 10844
 
9.4%

Length

2024-08-09T18:56:59.623920image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sp 48724
42.2%
rj 15029
 
13.0%
mg 13406
 
11.6%
rs 6359
 
5.5%
pr 5850
 
5.1%
sc 4223
 
3.7%
ba 3971
 
3.4%
df 2432
 
2.1%
go 2367
 
2.0%
es 2315
 
2.0%
Other values (17) 10844
 
9.4%

Most occurring characters

ValueCountFrequency (%)
S 62847
27.2%
P 58799
25.4%
R 28170
12.2%
M 16343
 
7.1%
G 15773
 
6.8%
J 15029
 
6.5%
A 6683
 
2.9%
E 6091
 
2.6%
C 5842
 
2.5%
B 4599
 
2.0%
Other values (7) 10864
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 231040
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 62847
27.2%
P 58799
25.4%
R 28170
12.2%
M 16343
 
7.1%
G 15773
 
6.8%
J 15029
 
6.5%
A 6683
 
2.9%
E 6091
 
2.6%
C 5842
 
2.5%
B 4599
 
2.0%
Other values (7) 10864
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 231040
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 62847
27.2%
P 58799
25.4%
R 28170
12.2%
M 16343
 
7.1%
G 15773
 
6.8%
J 15029
 
6.5%
A 6683
 
2.9%
E 6091
 
2.6%
C 5842
 
2.5%
B 4599
 
2.0%
Other values (7) 10864
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 231040
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 62847
27.2%
P 58799
25.4%
R 28170
12.2%
M 16343
 
7.1%
G 15773
 
6.8%
J 15029
 
6.5%
A 6683
 
2.9%
E 6091
 
2.6%
C 5842
 
2.5%
B 4599
 
2.0%
Other values (7) 10864
 
4.7%

seller_zip_code_prefix
Real number (ℝ)

HIGH CORRELATION 

Distinct2198
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24480.427
Minimum1001
Maximum99730
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-08-09T18:56:59.698505image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile2963
Q16429
median13660
Q328470
95-th percentile88330
Maximum99730
Range98729
Interquartile range (IQR)22041

Descriptive statistics

Standard deviation27613.774
Coefficient of variation (CV)1.127994
Kurtosis0.91808654
Mean24480.427
Median Absolute Deviation (MAD)8130
Skewness1.5511073
Sum2.8279789 × 109
Variance7.6252052 × 108
MonotonicityNot monotonic
2024-08-09T18:56:59.789301image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14940 8170
 
7.1%
5849 2137
 
1.8%
15025 2089
 
1.8%
9015 1853
 
1.6%
13405 1650
 
1.4%
4782 1545
 
1.3%
8577 1538
 
1.3%
3204 1462
 
1.3%
4160 1267
 
1.1%
13232 1254
 
1.1%
Other values (2188) 92555
80.1%
ValueCountFrequency (%)
1001 22
 
< 0.1%
1021 41
 
< 0.1%
1022 5
 
< 0.1%
1023 5
 
< 0.1%
1026 305
0.3%
1031 122
 
0.1%
1035 18
 
< 0.1%
1039 1
 
< 0.1%
1040 20
 
< 0.1%
1041 2
 
< 0.1%
ValueCountFrequency (%)
99730 12
 
< 0.1%
99700 2
 
< 0.1%
99670 1
 
< 0.1%
99500 59
0.1%
99300 2
 
< 0.1%
98975 19
 
< 0.1%
98920 2
 
< 0.1%
98910 13
 
< 0.1%
98803 62
0.1%
98780 4
 
< 0.1%
Distinct599
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2024-08-09T18:56:59.951325image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length40
Median length31
Mean length10.103385
Min length2

Characters and Unicode

Total characters1167143
Distinct characters41
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)0.1%

Sample

1st rowmaua
2nd rowmaua
3rd rowmaua
4th rowmaua
5th rowmaua
ValueCountFrequency (%)
sao 35839
 
18.0%
paulo 29132
 
14.7%
ibitinga 8170
 
4.1%
rio 5808
 
2.9%
do 5433
 
2.7%
preto 5423
 
2.7%
jose 4042
 
2.0%
de 4004
 
2.0%
santo 3206
 
1.6%
andre 3106
 
1.6%
Other values (630) 94580
47.6%
2024-08-09T18:57:00.230973image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 194252
16.6%
o 143043
12.3%
i 99756
 
8.5%
83283
 
7.1%
r 75939
 
6.5%
s 74391
 
6.4%
e 62661
 
5.4%
u 61195
 
5.2%
p 57413
 
4.9%
l 55577
 
4.8%
Other values (31) 259633
22.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1082645
92.8%
Space Separator 83283
 
7.1%
Other Punctuation 610
 
0.1%
Modifier Symbol 369
 
< 0.1%
Dash Punctuation 164
 
< 0.1%
Close Punctuation 31
 
< 0.1%
Open Punctuation 31
 
< 0.1%
Decimal Number 8
 
< 0.1%
Nonspacing Mark 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 194252
17.9%
o 143043
13.2%
i 99756
9.2%
r 75939
 
7.0%
s 74391
 
6.9%
e 62661
 
5.8%
u 61195
 
5.7%
p 57413
 
5.3%
l 55577
 
5.1%
t 46248
 
4.3%
Other values (14) 212170
19.6%
Other Punctuation
ValueCountFrequency (%)
' 345
56.6%
/ 139
22.8%
. 76
 
12.5%
@ 38
 
6.2%
\ 6
 
1.0%
, 6
 
1.0%
Decimal Number
ValueCountFrequency (%)
4 2
25.0%
2 2
25.0%
5 2
25.0%
0 1
12.5%
8 1
12.5%
Space Separator
ValueCountFrequency (%)
83283
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 369
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 164
100.0%
Close Punctuation
ValueCountFrequency (%)
) 31
100.0%
Open Punctuation
ValueCountFrequency (%)
( 31
100.0%
Nonspacing Mark
ValueCountFrequency (%)
̃ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1082645
92.8%
Common 84496
 
7.2%
Inherited 2
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 194252
17.9%
o 143043
13.2%
i 99756
9.2%
r 75939
 
7.0%
s 74391
 
6.9%
e 62661
 
5.8%
u 61195
 
5.7%
p 57413
 
5.3%
l 55577
 
5.1%
t 46248
 
4.3%
Other values (14) 212170
19.6%
Common
ValueCountFrequency (%)
83283
98.6%
´ 369
 
0.4%
' 345
 
0.4%
- 164
 
0.2%
/ 139
 
0.2%
. 76
 
0.1%
@ 38
 
< 0.1%
) 31
 
< 0.1%
( 31
 
< 0.1%
\ 6
 
< 0.1%
Other values (6) 14
 
< 0.1%
Inherited
ValueCountFrequency (%)
̃ 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1166772
> 99.9%
None 369
 
< 0.1%
Diacriticals 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 194252
16.6%
o 143043
12.3%
i 99756
 
8.5%
83283
 
7.1%
r 75939
 
6.5%
s 74391
 
6.4%
e 62661
 
5.4%
u 61195
 
5.2%
p 57413
 
4.9%
l 55577
 
4.8%
Other values (29) 259262
22.2%
None
ValueCountFrequency (%)
´ 369
100.0%
Diacriticals
ValueCountFrequency (%)
̃ 2
100.0%

seller_state
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
SP
82412 
MG
9009 
PR
8900 
RJ
 
4883
SC
 
4223
Other values (17)
 
6093

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters231040
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSP
2nd rowSP
3rd rowSP
4th rowSP
5th rowSP

Common Values

ValueCountFrequency (%)
SP 82412
71.3%
MG 9009
 
7.8%
PR 8900
 
7.7%
RJ 4883
 
4.2%
SC 4223
 
3.7%
RS 2225
 
1.9%
DF 931
 
0.8%
BA 697
 
0.6%
GO 539
 
0.5%
PE 465
 
0.4%
Other values (12) 1236
 
1.1%

Length

2024-08-09T18:57:00.335565image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sp 82412
71.3%
mg 9009
 
7.8%
pr 8900
 
7.7%
rj 4883
 
4.2%
sc 4223
 
3.7%
rs 2225
 
1.9%
df 931
 
0.8%
ba 697
 
0.6%
go 539
 
0.5%
pe 465
 
0.4%
Other values (12) 1236
 
1.1%

Most occurring characters

ValueCountFrequency (%)
P 91838
39.7%
S 89304
38.7%
R 16078
 
7.0%
M 9625
 
4.2%
G 9548
 
4.1%
J 4883
 
2.1%
C 4325
 
1.9%
A 1117
 
0.5%
E 954
 
0.4%
D 931
 
0.4%
Other values (6) 2437
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 231040
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 91838
39.7%
S 89304
38.7%
R 16078
 
7.0%
M 9625
 
4.2%
G 9548
 
4.1%
J 4883
 
2.1%
C 4325
 
1.9%
A 1117
 
0.5%
E 954
 
0.4%
D 931
 
0.4%
Other values (6) 2437
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 231040
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 91838
39.7%
S 89304
38.7%
R 16078
 
7.0%
M 9625
 
4.2%
G 9548
 
4.1%
J 4883
 
2.1%
C 4325
 
1.9%
A 1117
 
0.5%
E 954
 
0.4%
D 931
 
0.4%
Other values (6) 2437
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 231040
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 91838
39.7%
S 89304
38.7%
R 16078
 
7.0%
M 9625
 
4.2%
G 9548
 
4.1%
J 4883
 
2.1%
C 4325
 
1.9%
A 1117
 
0.5%
E 954
 
0.4%
D 931
 
0.4%
Other values (6) 2437
 
1.1%

Interactions

2024-08-09T18:56:52.285995image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:48.223819image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:48.957338image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:49.508824image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:50.036081image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:50.659089image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:51.189210image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:51.745403image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:52.357196image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:48.387506image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:49.032127image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:49.579026image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:50.107890image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:50.729859image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:51.262294image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:51.817968image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:52.426521image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:48.525697image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:49.100633image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:49.645982image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:50.176848image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:50.797670image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:51.334472image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:51.887303image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:52.489466image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:48.596078image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:49.164529image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:49.706302image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:50.239045image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:50.859749image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:51.399696image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:51.951131image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:52.559140image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:48.669603image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:49.234176image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:49.772717image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:50.304869image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:50.926512image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:51.471079image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:52.018682image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:52.624792image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:48.739551image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:49.301106image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:49.838122image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:50.453138image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:50.988889image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:51.539549image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:52.085120image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:52.693563image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:48.815588image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:49.372502image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:49.907451image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:50.525598image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:51.059583image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:51.608532image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:52.155594image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:52.758582image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:48.885506image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:49.440099image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:49.971616image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:50.592832image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:51.124312image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:51.676094image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-08-09T18:56:52.221639image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2024-08-09T18:57:00.389661image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
customer_statecustomer_zip_code_prefixfreight_valueorder_item_idorder_statuspayment_installmentspayment_sequentialpayment_typepayment_valuepriceseller_stateseller_zip_code_prefix
customer_state1.000-0.721-0.4600.0150.030-0.0730.0020.038-0.097-0.0690.055-0.045
customer_zip_code_prefix-0.7211.0000.466-0.0090.0260.070-0.0060.0330.1060.0700.0650.060
freight_value-0.4600.4661.000-0.0560.0180.1910.0180.0100.4240.4350.0480.257
order_item_id0.015-0.009-0.0561.0000.0050.060-0.0070.0210.256-0.1160.000-0.013
order_status0.0300.0260.0180.0051.0000.0030.0020.0060.0020.0120.0120.005
payment_installments-0.0730.0700.1910.0600.0031.000-0.1760.2720.3970.3180.0330.066
payment_sequential0.002-0.0060.018-0.0070.002-0.1761.0000.227-0.212-0.0050.0170.007
payment_type0.0380.0330.0100.0210.0060.2720.2271.000-0.0920.0330.0210.003
payment_value-0.0970.1060.4240.2560.0020.397-0.212-0.0921.0000.7900.0370.163
price-0.0690.0700.435-0.1160.0120.318-0.0050.0330.7901.0000.0530.179
seller_state0.0550.0650.0480.0000.0120.0330.0170.0210.0370.0531.000-0.751
seller_zip_code_prefix-0.0450.0600.257-0.0130.0050.0660.0070.0030.1630.179-0.7511.000

Missing values

2024-08-09T18:56:53.071290image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-09T18:56:53.452897image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

order_idcustomer_idorder_statusorder_purchase_timestamporder_item_idproduct_idseller_idpricefreight_valuepayment_sequentialpayment_typepayment_installmentspayment_valueproduct_category_namecustomer_unique_idcustomer_zip_code_prefixcustomer_citycustomer_stateseller_zip_code_prefixseller_cityseller_state
0e481f51cbdc54678b7cc49136f2d6af79ef432eb6251297304e76186b10a928ddelivered2017-10-02 10:56:331.087285b34884572647811a353c7ac498a3504c0cb71d7fa48d967e0e4c94d59d929.998.721.0credit_card1.018.12housewares7c396fd4830fd04220f754e42b4e5bff3149sao pauloSP9350.0mauaSP
1e481f51cbdc54678b7cc49136f2d6af79ef432eb6251297304e76186b10a928ddelivered2017-10-02 10:56:331.087285b34884572647811a353c7ac498a3504c0cb71d7fa48d967e0e4c94d59d929.998.723.0voucher1.02.00housewares7c396fd4830fd04220f754e42b4e5bff3149sao pauloSP9350.0mauaSP
2e481f51cbdc54678b7cc49136f2d6af79ef432eb6251297304e76186b10a928ddelivered2017-10-02 10:56:331.087285b34884572647811a353c7ac498a3504c0cb71d7fa48d967e0e4c94d59d929.998.722.0voucher1.018.59housewares7c396fd4830fd04220f754e42b4e5bff3149sao pauloSP9350.0mauaSP
3128e10d95713541c87cd1a2e48201934a20e8105f23924cd00833fd87daa0831delivered2017-08-15 18:29:311.087285b34884572647811a353c7ac498a3504c0cb71d7fa48d967e0e4c94d59d929.997.781.0credit_card3.037.77housewares3a51803cc0d012c3b5dc8b7528cb05f73366sao pauloSP9350.0mauaSP
40e7e841ddf8f8f2de2bad69267ecfbcf26c7ac168e1433912a51b924fbd34d34delivered2017-08-02 18:24:471.087285b34884572647811a353c7ac498a3504c0cb71d7fa48d967e0e4c94d59d929.997.781.0credit_card1.037.77housewaresef0996a1a279c26e7ecbd737be23d2352290sao pauloSP9350.0mauaSP
5bfc39df4f36c3693ff3b63fcbea9e90a53904ddbea91e1e92b2b3f1d09a7af86delivered2017-10-23 23:26:461.087285b34884572647811a353c7ac498a3504c0cb71d7fa48d967e0e4c94d59d929.9914.101.0boleto1.044.09housewarese781fdcc107d13d865fc7698711cc57288032florianopolisSC9350.0mauaSP
68736140c61ea584cb4250074756d8f3bab8844663ae049fda8baf15fc928f47fdelivered2017-08-10 13:35:551.0b00a32a0b42fd65efb58a5822009f6293504c0cb71d7fa48d967e0e4c94d59d975.907.791.0credit_card1.083.69baby02c9e0c05a817d4562ec0e8c90f29dba8577itaquaquecetubaSP9350.0mauaSP
788407c8c6e12493ff6e845df39540112e902cb9d9992a69a267f69dec57aa3a3delivered2017-08-15 02:03:011.0b00a32a0b42fd65efb58a5822009f6293504c0cb71d7fa48d967e0e4c94d59d975.907.791.0credit_card2.083.69baby28adbfbaf0b9c5e5a0555a8c853a753413060campinasSP9350.0mauaSP
84f2acff0b7d2bcc4a408abe5a223d407d67b6cca5a87299f711a6961f579fe67delivered2017-08-01 16:31:351.0b00a32a0b42fd65efb58a5822009f6293504c0cb71d7fa48d967e0e4c94d59d975.9014.281.0boleto1.090.18babyaea90564d6f09ae11bf936f55ed49d7282030curitibaPR9350.0mauaSP
9019aaee09698daf81dcffe9d94a18b5ce3893e579755de4feb1a4d0313c103fadelivered2017-08-10 14:04:581.0b00a32a0b42fd65efb58a5822009f6293504c0cb71d7fa48d967e0e4c94d59d975.907.791.0credit_card2.083.69babycd6b577df45c00daa6b2767eaa947c7213092campinasSP9350.0mauaSP
order_idcustomer_idorder_statusorder_purchase_timestamporder_item_idproduct_idseller_idpricefreight_valuepayment_sequentialpayment_typepayment_installmentspayment_valueproduct_category_namecustomer_unique_idcustomer_zip_code_prefixcustomer_citycustomer_stateseller_zip_code_prefixseller_cityseller_state
118424f5f8998eee8ec7bc513dc52847d64ce0f4656b824844a039a87fd9c51ad3586acanceled2018-03-01 11:42:231.051bd37bb8517d5bfdb1f54c11fb01d27f09e26011d833ddab11593c1a097a92a79.9022.191.0credit_card2.0102.09furniture_decor149164aee69ed656dedbbe68623157bc13469americanaSP13632.0pirassunungaSP
1184255bacbd9f42bd029c3a296501224e193e5a1470d43d8ad960d4199134d3df48e0delivered2018-08-10 21:14:351.0710e8b076db06c8e5343a9e23f0e3d838dd386be0767c330276ea6a3f96532d344.9922.251.0credit_card2.0134.48sports_leisure0b39f417a3c099ff0497346258e8d75239810caraiMG88490.0paulo lopesSC
1184265bacbd9f42bd029c3a296501224e193e5a1470d43d8ad960d4199134d3df48e0delivered2018-08-10 21:14:352.0710e8b076db06c8e5343a9e23f0e3d838dd386be0767c330276ea6a3f96532d344.9922.251.0credit_card2.0134.48sports_leisure0b39f417a3c099ff0497346258e8d75239810caraiMG88490.0paulo lopesSC
1184275a8a4dc28b16fb90469ad749f9535773c0c8b8bb055100a0cc08dcc04d847ac9canceled2018-03-13 10:58:091.033ac889bc3af4ddede9c14fc789a3743666658b8da8370f30e1f89893b1de5e6149.0011.671.0boleto1.0321.34garden_tools82ec5f749b66f1857e868b6414a67ab36765taboao da serraSP3658.0sao pauloSP
1184285a8a4dc28b16fb90469ad749f9535773c0c8b8bb055100a0cc08dcc04d847ac9canceled2018-03-13 10:58:092.033ac889bc3af4ddede9c14fc789a3743666658b8da8370f30e1f89893b1de5e6149.0011.671.0boleto1.0321.34garden_tools82ec5f749b66f1857e868b6414a67ab36765taboao da serraSP3658.0sao pauloSP
1184291ab38815794efa43d269d62b98dae815a0b67404d84a70ef420a7f99ad6b190adelivered2018-07-01 10:23:101.031ec3a565e06de4bdf9d2a511b822b4dbabcc0ab201e4c60188427cae51a5b8b79.0014.131.0boleto1.093.13construction_tools_lights2077f7ec37df79c62cc24b7b8f30e8c98528ferraz de vasconcelosSP13660.0porto ferreiraSP
118430b159d0ce7cd881052da94fa165617b05e0c3bc5ce0836b975d6b2a8ce7bb0e3ecanceled2017-03-11 19:51:361.0241a1ffc9cf969b27de6e723010202688501d82f68d23148b6d78bb7c4a4203719.7010.961.0credit_card1.030.66auto78a159045124eb7601951b917a42034f89111gasparSC89031.0blumenauSC
118431735dce2d574afe8eb87e80a3d6229c48d531d01affc2c55769f6b9ed410d8d3cdelivered2018-07-24 09:46:271.01d187e8e7a30417fda31e85679d96f0fd263fa444c1504a75cbca5cc465f592a399.0045.071.0debit_card1.0444.07furniture_decor8cf3c6e1d2c8afaab2eda3fa01d4e3d260455fortalezaCE13478.0americanaSP
11843225d2bfa43663a23586afd12f15b542e79d8c06734fde9823ace11a4b5929b5a7delivered2018-05-22 21:13:211.06e1c2008dea1929b9b6c27fa01381e90edf3fabebcc20f7463cc9c53da932ea8219.9024.121.0credit_card4.0244.02furniture_decore55e436481078787e32349cee9febf5e39803teofilo otoniMG8320.0sao pauloSP
1184331565f22aa9452ff278638e87cc89567856772dfbcbe7df908a284ff0d53adf7ddelivered2018-05-15 17:41:001.09c1e194db1d35a79d962ea610bfe0868f3862c2188522d89860c38a3ea8b550d15.5012.791.0boleto1.028.29perfumery6ceea7c1088e15ab3c67980a2d9bb3099687sao bernardo do campoSP14092.0ribeirao pretoSP